Why Data Segmentation Is Not Enough
People Are More Complicated Than Their Data Segmentation Parameters
When evaluating customer wants and needs, there are many ways of looking at data from the sublime to ridiculous. Commonly accepted ways of looking at customer data include generating a “gut feel” from experience, sampling at random,looking at exceptions, reviewing periodic totals, and SQL selection. SQL, the most common tool used by database marketers, stands for Structured Query Language, but what it boils down to is averages or data segmentation. It gives you general information about groups of people, often by age or income.
Let’s say, for instance, that you want to find people that fit a certain category, such as empty-nesters. To use data segmentation to find these people, you might search for people in the 50 – 60 year age range and within a certain income bracket. SQL would look at your data to find the people who fit both of these parameters.
The problem with SQL is that you’re potentially overlooking the right people. In this example, your data segment would be missing all of the empty nesters who are under 50 as well as those who fall outside your chosen income range. SQL is just a fancy way to put people into buckets. The problem is, not everyone you seek is in the obvious bucket.
People are more complicated than just their income or age, but when analyzing populations, you can become trapped by these segmentation tools and rules.
Where Your Customers Really Are
The truth is that you’re not really looking for empty-nesters within certain parameters (shown in brown on the chart) or even empty-nesters in general. You are looking for people who will be receptive to what you have to offer (shown in yellow on the chart).
To find these potential customers, you need to stop relying on buckets and start looking at the bigger picture. And to do that, you need statistics. Statistics can help you find the best people that correspond to your potential customer base.
Predictive Analytics: Better than Buckets
The most effective form of selection would be to use the most information and use it optimally. With this light, predictive analytics can be seen as the clear winner for selecting target prospects or customers.
Predicative analytics looks at the bigger picture of marketing. It has the capability to consider all kinds of factors, rather than just one of two. It also looks at patterns in behavior, and makes predictions about future behaviors. This lets you more accurately pinpoint your potential customers, as well as stop marketing to people who are not interested in what you have to offer.
That sounds like a much more effective way to do business, right? The problem is that you can’t do it by yourself. Predictive analytics is a science, and it can take years to develop accurate predictive models. You don’t want to make the top 10 mistakes a novice with predictive analytics would make. That’s why turning to a reputable predictive analytics provider can save you time and money.
At Stics, we have spent year’s fine tuning our software and processes so our customers get extremely accurate predictions. We can take your data and give you insightful information about potential customers and the effectiveness of future marketing campaigns. With Stics predictive analytics solutions you can make your business more efficient and cost-effective.
The Data Fact Gap: What You Don’t Know Can Hurt You
June 18, 2010 by Stics·
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What the Data Fact Gap Means for You
Fact Gap Graph
As professionals, we all know that technology has changed the way we do business. Whether you find the increased dependence on new technology as good or bad often depends on how effectively the tools are used. Over the years, this problem has been illustrated in many ways by me and by others. This particular Fact Gap illustration was first attributed to the Gartner Group. It will help me describe how data technologies are, on the one hand, progressing and on the other, creating new data analysis problems.
The Data Fact Gap was created by the explosion of available digital information accumulated in recent years. With technology system advances, increased data storage capacity and Internet usage it is now easy to collect mountains of data. While the volume of retained data has grown exponentially and spread across all industries, so have the data management challenges it created and the even greater marketing opportunity that mostly lays dormant.
This abundance of data creates new problems that force database marketers to devote a lot of time and resources to filtering information into data segments so decision makers can frame a concept, problem or question. While this approach is intuitive to the human brain, it does limit our ability to make a fully informed decision from all available data.
Why You Need Good Data
Intuitively we often think we already know what our customers want. However, that is not always the case. When we make business decisions by filtering our data down to a few variables we miss the more accurate and complete view of the data. Without hard data, there’s no way to be sure truly objective decisions are being made. Worse, because we think we’re making objective decisions, we often don’t seek outside an perspective.
What we really need is an objective analysis, wielding as many customer factors and data points as possible. This approach helps us see the potential hidden below the common database marketing analysis.
Statistical Predictive Analytics Solves the Problem
One way to harness the data explosion and make better marketing and business decisions is to use predictive analytics. Predictive analytics uses the science of statistics and is capable of considering unlimited facets of a situation. Predictive analytics for marketing can increase a marketing campaigns return on investment by ten times compared to a typical SQL analysis that might only evaluates about five variables. It takes the data that you already have and give you information you can use in your marketing campaigns, such as:
- Customers you are currently marketing to, who are unprofitable
- High value customers or prospects you are not marketing to
- More profitable marketing programs
- Respective value of various members in your customer base
Statistical modeling with predictive analytics is proven to help make more informed decisions and increase profit margins.
In my business, we live on the front lines of customer data knowledge generation and have a deep understanding of the problems and opportunities created by the data explosion. For that reason, we discourage installing expensive applications that might not provide quick answers to important questions that drive revenue and profitability. We know that with seasoned expertise, a leading statistical technology platform and focused services like ours, it is possible to bridge the data gap and quickly and easily improve the performance of your marketing campaigns.
Stics offers innovative solutions that provide deeper insight into your customer and loyalty database for greater marketing return on investment. We welcome inquiries and are happy to answer questions.
